Air quality data restoration based on graph regularization multi-view functional matrix completion.

	Air quality data restoration based on graph regularization multi-view functional matrix completion.
其他题名	基于图正则化多视角函数型矩阵填充的空气质量数据修复
	Gao, Hai-Yan1,2 ; Ma, Wen-Juan1
	2024
发表期刊	Zhongguo Huanjing Kexue/China Environmental Science
卷号	44 期号:10 页码:5357-5370
摘要	Due to issues such as sensor malfunctions and data transmission, the collected air quality data often encounter challenges of sparsity and incompleteness. In order to effectively repair and reconstruct the missing parts of air quality data, a Graph Regularized Multi-view Functional Matrix Completion method (GRMFMC) is proposed. Firstly, this innovative method introduces a graph regularization approach that thoroughly takes into account the high-order neighborhood relationship within each pollutant’s sample set, reducing information loss. Secondly, it utilizes the Hilbert-Schmidt Independence Criterion (HSIC) to discern complementary information among various pollutants, thereby improving imputation accuracy. Additionally, by integrating the principles of functional data analysis, the GRMFMC technique treats temporal air quality data as continuous functions, capitalizing on their inherent smoothness and correlation for high-precision data interpolation. Simulation imputations and empirical applications on real air quality datasets both demonstrate that the GRMFMC exhibits superior interpolation performance. In simulation imputations, the GRMFMC method reduces the imputation error by 56%~99% in RMSE and 46%~98% in NRMSE; in empirical applications, it reduces the error by 51%~99% in RMSE and 40%~98% in NRMSE. Furthermore, the GRMFMC method shows consistent robustness across different missing rate and pollutant categories, confirming its potential for generalization capability and practical value in professional settings. © 2024 Chinese Society for Environmental Sciences. All rights reserved.
关键词	Air quality Data assimilation Data integration Data reduction Matrix algebra Metadata Network security Air quality data Completion methods Data restoration Functional data analysis Functional matrix Graph regularization Matrix completion Multi-view learning Multi-views Regularisation
收录类别	EI
ISSN	1000-6923
语种	中文
出版者	Chinese Society for Environmental Sciences
EI入藏号	20244317271209
EI主题词	Spatio-temporal data
EI分类号	1106 ; 1106.2 ; 1106.4 ; 1201.1 ; 1502.1.1.1.1 ; 1502.1.1.4.1
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/38340
专题	统计与数据科学学院
通讯作者	Gao, Hai-Yan
作者单位	1.School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 2.Key Laboratory of Digital Economy and Social Computing Science, Lanzhou; 730020, China
第一作者单位	统计与数据科学学院
通讯作者单位	统计与数据科学学院
推荐引用方式 GB/T 7714	Gao, Hai-Yan,Ma, Wen-Juan. Air quality data restoration based on graph regularization multi-view functional matrix completion.[J]. Zhongguo Huanjing Kexue/China Environmental Science,2024,44(10):5357-5370.
APA	Gao, Hai-Yan,&Ma, Wen-Juan.(2024).Air quality data restoration based on graph regularization multi-view functional matrix completion..Zhongguo Huanjing Kexue/China Environmental Science,44(10),5357-5370.
MLA	Gao, Hai-Yan,et al."Air quality data restoration based on graph regularization multi-view functional matrix completion.".Zhongguo Huanjing Kexue/China Environmental Science 44.10(2024):5357-5370.